Package Recipe 'bioconductor-eegc'

recipe bioconductor-eegc

Engineering Evaluation by Gene Categorization (eegc)

Homepage:: https://bioconductor.org/packages/3.17/bioc/html/eegc.html
License:: GPL-2
Recipe:: /bioconductor-eegc/meta.yaml

This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic cells to primary cells based on high throughput gene expression data screened either by DNA microarray or RNA sequencing. The package takes gene expression profiles as inputs from three types of samples: (i) somatic or stem cells to be (trans)differentiated (input of the engineering process), (ii) induced cells to be evaluated (output of the engineering process) and (iii) target primary cells (reference for the output). The package performs differential gene expression analysis for each pair-wise sample comparison to identify and evaluate the transcriptional differences among the 3 types of samples (input, output, reference). The ideal goal is to have induced and primary reference cell showing overlapping profiles, both very different from the original cells.

package bioconductor-eegc¶

Versions:

1.26.0-0, 1.24.0-0, 1.20.0-0, 1.18.0-0, 1.16.0-1, 1.16.0-0, 1.14.0-0, 1.12.0-0, 1.10.0-1,

1.26.0-0, 1.24.0-0, 1.20.0-0, 1.18.0-0, 1.16.0-1, 1.16.0-0, 1.14.0-0, 1.12.0-0, 1.10.0-1, 1.8.1-0

Depends:

on bioconductor-annotationdbi >=1.62.0,<1.63.0
on bioconductor-clusterprofiler >=4.8.0,<4.9.0
on bioconductor-deseq2 >=1.40.0,<1.41.0
on bioconductor-dose >=3.26.0,<3.27.0
on bioconductor-edger >=3.42.0,<3.43.0
on bioconductor-limma >=3.56.0,<3.57.0
on bioconductor-org.hs.eg.db >=3.17.0,<3.18.0
on bioconductor-org.mm.eg.db >=3.17.0,<3.18.0
on bioconductor-s4vectors >=0.38.0,<0.39.0
on r-base >=4.3,<4.4.0a0
on r-ggplot2
on r-gplots
on r-igraph
on r-pheatmap
on r-r.utils
on r-sna
on r-wordcloud

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-eegc

to add into an existing workspace instead, run:

pixi add bioconductor-eegc

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-eegc

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-eegc

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-eegc:<tag>

(see bioconductor-eegc/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-eegc/README.html)